The Ultimate Guide to Embedded Analytics [2022]

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Embedded Analytics General

What is Embedded Analytics

Embedded analytics is the integration of analytic content and capabilities within applications, such as business process applications (e.g. CRM, ERP, EHR/EMR) or portals (e.g. intranets or extranets).

Incorporating relevant data and analytics inside applications helps users work smarter and more efficiently by solving high-value business problems. More importantly, embedded analytics is a tool to make data accessible to non-technical users. 

Embedded analytics - your Product your Branding

So how is embedded analytics different from traditional BI?

Embedded Analytics vs. Traditional BI


Business intelligence (BI) is a set of independent systems (technologies, processes, people, etc.) that aggregate data from multiple sources, prepare the data for analysis, and then provide reporting and analysis on that data from a central viewpoint. It is most optimized for supporting management-level decisions that require an aggregated view of information from across a department, function, or entire organization. These systems are specifically designed for people whose sole responsibility is to perform data analysis.

Embedded analytics is a set of capabilities that are tightly integrated into existing applications (like your CRM, ERP, financial systems, and/or information portals) that bring additional awareness, context, or analytic capability to support decision-making related to a variety of tasks. These tasks may require data from multiple systems or aggregated views, but the output is more than a centralized overview of information. It is targeted information to support a decision or action in the context in which that decision or action takes place. Comparatively, traditional business intelligence involves extracting insights from data within the silo of analysis.

For decades, BI and analytics tools have failed to penetrate more than 25% of an organization. And within that 25%, most workers use the tools only once or twice a week. Embedded analytics changes this equation. By inserting charts, dashboards, and entire authoring and administrative environments inside other applications, embedded analytics dramatically increase BI adoption. The catch is that most business users don’t know they’re “using BI”— it’s just part of the application they already use. 

Let's use an analogy: 

Business Intelligence - Map Analogy

The business intelligence tool is like a map that we use to plan the itinerary before a long road trip. Embedded analytics is the GPS navigation inside your car that guides your way in real time! So, turn up the volume, and enjoy the view.

More specifically, BI software is not integrated into the experience offered by your software or platform. These tools require navigation between separate interfaces to view reports, which means users have to go back and forth between multiple windows.

BI solution gives you an overview of data coming from many systems, but without offering an easily understandable context. On the contrary, thanks to embedded analytics, it becomes possible to process data from multiple sources and integrate reports into your solution.

With the evolution of in-app analytics and the business’ response to it, a fundamental shift has occurred in the way we consume data. Visualizations used to entail static pictures and charts that presented a snapshot of a single point in time, but this is no longer the case. Analytics, interactive visualizations and real-time reports are increasingly necessary to turn the massive volume of data into compelling stories. Forrester predicts embedded analytics will become the new normal in three to five years due to the increase in operational and business complexity.

Types of embedded analytics

Embedded analytics strives to bring together insight and action by integrating analytics deeper and deeper within business applications and workflows. The Analytics embedded within applications can have various types of architecture.

Embedded Analytics - Types

Standalone analytics. Generally speaking, standalone BI and/or data visualization tools are built to create custom views of multiple data sources as well as help you look at other relevant data sources together or within a wider context. They also allow IT and software developers to embed analytics into their applications, enabling them to mash and merge data in order to answer complex business questions. The resulting visualizations can then be embedded into applications to make it easier for you to benefit from the insights.

Detached analytics. This is a “diet” version of embedding where the host and analytics run separately but are tightly linked via URLs. This approach works well when multiple applications use the same analytics environment, portal, or service. There is no commonality between the host application and analytics tool, except for a shared URL and shared data. The two applications might also share a common authentication mechanism to facilitate single sign-on (SSO).

Inline analytics. With inline analytics, the analytics tool is part of the host application. It looks, feels, and acts as the host but runs as a separate element or module within it. There might also be a separate tab where customers can view analytics about their activity within the application. In most cases, the embedded components sit within an iFrame, which is an HTML container that runs inside a webpage.

Fused analytics. Fused analytics delivers the tightest level of integration with a host application. Here, the analytics (e.g., a chart, table, or graphical component) sit side by side with the host application components and communicate bi-directionally with them. Fused analytics is facilitated by JavaScript libraries that control front-end displays and REST API calls that activate server functions. 

Why are companies embracing embedded analytics?

Today, Over 90% of software companies are embedding analytics tools in their applications, according to the State of Embedded Analytics Report. So why is embedded analytics garnering so much attention now in the software industry and business intelligence space and how are businesses using embedded analytics? Let’s look at some of the reasons.

Benefits of Embedded Analytics

Achieve faster time to market
Dedicated analytics development requires expertise, resources and time. It can be difficult to achieve speed, reliability and scale with a homegrown solution. By investing in a Software-as-a-Service (SaaS) embedded analytics solution, the burden of maintaining infrastructure, storage and computing power is removed and you can be confident your analytics solution will work at 5×, 10× or even 100× users. Perhaps most importantly, the right embedded analytics solution puts you in a position to go to market faster by drastically cutting down the analytics development cycle. 

Embedded Analytics - Faster Time to Market

" Empowered non-technical users to build robust, customizable dashboards that were fully integrated and live in <1 month "  

Check out Storyslab’s journey


Stand out in the marketplace 
Businesses today want to differentiate their offerings and increase the adoption rate of products in the customer’s hands. Only 25% of users return to an application after their first day using it. The key to better traction is delivering insights to customers anywhere, anytime, in less than 3 steps. While traditional analytics solutions often require significant data wrangling, embedded analytics delivers on-demand insights within applications in one easy context. The ability to quickly and intuitively drill down in a report, cross-highlight it with other relevant data or perform trend forecasting delivers significant value to users. In fact, the difference in usage between embedded analytics and non-embedded solutions is significant: 43% of users leverage embedded analytics on a regular basis – double the adoption rate of traditional analytics. The trend is clear: the more accessible analytics are in your app, the more customers will adopt them.

Expand revenue streams
An embedded analytics solution can impact your own business as much as it impacts your application. Embedded analytics can open the door to new revenue streams and help grow opportunity size. You can develop new lines of business around embedded analytics services – such as white labeled dashboards – as ways to expand customer loyalty and scale your business. This allows you to unlock more upselling and cross-selling opportunities.

Embedded Analytics - Expand Revenue

"Generated new revenues by addressing new personas and empowered their 150+ users with data"

Learn more about Dataneo’s journey


Generate value, not just reports 
Your application developers should focus on creating new features and improving core functionality, not custom building an extensive analytics engine or coding reporting features that need to be constantly updated. With the right embedded analytics, you don’t have to start from scratch to meet customer demands. This puts time back into the hands of your developers to improve core features and functionalities. 

The more an application can guide users through decisions by weaving massive sets of data into something meaningful, the more likely adoption and usage will increase. As a software vendor, you need a combination of powerful analytics and visualization capabilities in order to deliver on the promise of turning data into actionable insight. 


Reap the rewards of others’ dedicated BI investments 
As soon as a feature becomes “industry standard” and is noticeably lacking from an application, it creates a challenge for businesses trying to grow their user base. And what if you want to integrate world-class analytics into your application? By leveraging the dedicated R&D of an analytics vendor, you lift a significant development burden from the shoulders of your developers and product managers. Critical updates and new features are automatically integrated into your application, and you get to reap the rewards of satisfied customers

Leverage a community of support 
The additional value provided by a community of experts, support staff and customer care shouldn’t be underestimated. Dedicated onboarding, with a step-by-step explanation and video learning content, can get your teams working faster and help build expertise in your business. Global communities of users may also lead to partnerships, inspire new offerings or just help you troubleshoot a tricky issue. 


Self-Service Analytics for Everyone
Data analysis is no longer for just a few highly trained, technical people. Ten years ago, the IT department and analysts were responsible for most analytics and information delivery. Today, everyone needs to be a “data expert” in their own role to make intelligent decisions that drive business forward. This influx of non-technical users has forced embedded analytics solutions to re-work the definition of self-service analytics. Users expect the software to simplify the traditionally difficult tasks of data preparation, data querying, and visual analysis.

Embedded Analytics- Self Service

"Kenzai uses data storytelling best practices to bridge the gap between complex data and everyday business users"


Empower customers with confidence 

From business executives and data scientists to the occasional user, providing actionable insights to anyone who uses your app will keep them coming back for more. This can be easily accomplished with the use of embedded analytics. At the end of the day, it’s all about driving success for your customers and that means giving them not only the tools they need but also the confidence that they are making the right decisions for their business. Today, every role is a data role and with embedded analytics, your customers can gain actionable insights that would directly drive growth.

Empower Users embedded analytics


The key to insight is embedded analytics 
Timely access to insights is more important than ever with growing data volumes and business complexity. But customers can’t make an informed decision based on raw data, they need the means to understand it. Business applications with easy-to-use analytics are the critical link between customers and the data they need. Simple charts don’t do justice to your application or customer challenges. To meet today’s complex business challenges, application data must be accessible anywhere and tell a story to inform insight. By purchasing a complete embedded analytics solution, you can make data exploration easy. Free your customers from exporting, give them fewer steps to insight and enable more powerful self-service.

From cost centric to profit-centric with embedded analytics 
Today, data volumes, sources and reporting complexity have grown, and the potential overhead has skyrocketed. This leaves a lot of businesses wondering how to best expand their analytics and visualization capabilities. Does a custom-built platform make the most sense for your business's unique needs, or will an off-the-shelf solution free you up to focus on what’s important to your business? Real-time, self-service and freely explorable visualizations provide massive competitive differentiation, but developing a homegrown set of visuals to represent your data strains the time and resources of software vendors and developers. Technical and business decision-makers alike know that building analytics from the ground up comes with serious challenges, and you’ve probably said something like this yourself

Embedded Analytics - Profit Centric

Mature, embedded analytics and BI solutions are available off the shelf, enabling your business to leverage the dedicated R&D, infrastructure and development effort of a vendor that has BI and data. If you don’t have this core competency in-house, choosing to buy is the obvious option. By eliminating the burden of development, your business will be free to invest in capturing additional value for you, your developers and your customers. 

So, how will you provide in-app analytics to your customers? Will you invest in building a custom solution in-house, or purchase an embedded analytics solution off the shelf?


Why build or buy

When faced with the need to embed analytics into an application, most software providers arrive at the crossroads of the “build versus buy” decision.

Why Build (or Really, Code)?

The first instinct for many application developers is to build the necessary reporting functionality with the help of code libraries or charting components. What invariably happens over time is that users ask for more functionality, more flexibility in their analysis, and more methods to gain insight without your help. Very few customers want to simply extract data into an excel file. Most customers these days want to be able to build custom dashboards and visualizations as they learn the product. With increasing demand it becomes difficult to build analytics in a scalable way.  

Application providers who stay on the “build” track are committing to staffing significant resources in developing, supporting, and keeping up with advances in data visualizations and business intelligence over the long term.

Building Analytics Cost Employees


Why Buy?

Many software organizations are under pressure from customers or competitors to improve analytics capabilities, and they do not have the time or resources to build on their own. In fact, in every survey conducted with software providers, the top reasons for embedding with a third-party product are:

  • Cost to build and maintain capabilities on their own – It can be expensive to initially develop, provide ongoing support, and continually enhance analytics capabilities.

  • Need to get to market faster – There is usually a small window of time available to satisfy customers, differentiate a product offering, and stand out in the marketplace.

  • Desire to have internal resources focused on core application functionality – Delivering functionality with a third party makes the development team more efficient and frees up resources for your core product.

Evaluating the build and the buy options requires an understanding of the targeted functionality to be implemented, the level of integration required and a cost/benefit analysis.

Defining the Time Frame
As a general rule of thumb, we take 3-5 years as the time frame in which we compare technology implements. So how will building your own analytics solution compare to embedding an analytics solution in this timeframe?






Benefits Summary

  • To deliver all the needed functionalities the “Build” options will take 3x to 4x the time compared to the Buy option.
  • High dependency on internal team distracting them from focusing on core competency
  • The need for constant coding updates and maintenance puts added pressure on developers in the team
  • Longer go to market lead to longer time to value, 
  • This slows down customer acquisition and adoption. 
  • In turn, reducing the ability to retain customers and lower selling prices for your product. 
  • Dependence on internal development resources to implement new functionality limits the predictability of delivering over the long term
  • The “Buy” option delivers a great product with seamless interaction within a month. 
  • There is a very low dependency on the internal team, letting them focus on the core competencies of the product. 
  • Updates are performed by the embedded analytics company and integrated into your product automatically, reducing pressure on developers. 
  • You go to market sooner, in as little as 1/4th the time as the Build option, 
  • This gives faster time to value
  • Which increases customer acquisition and adoption along with customer satisfaction
  • Resulting in higher customer retention, and higher average selling prices for your product.
  • Relying on embedded analytics with a wide range of functionality improves the clarity of your roadmap and your ability to meet changing customer demand.


Compared to coding on your own, utilizing a third-party product will get more capabilities in less time. The faster path to value usually drives the “buy” decision. If you are building quantitative ROI models, that difference in time will show up as achieving a breakeven point earlier in the project lifecycle.


Now we compare the ROI on embedded analytics.

By building a cost-benefit analysis over time, you can calculate the ROI for each buy or build option. Here is the ROI formula.

ROI [%] = Benefit / Costs  -1 

  • Benefits – This is a combination of strategic benefits (e.g., revenue increase) and operational benefits (e.g., cost reduction).

  • Costs – This is your investment to develop and maintain the solution.

  • “-1” – The formula assures that a positive ROI is achieved only when benefits exceed the costs.

If it's tough to understand the exact benefits and costs that an embed solution would offer, here is an ROI calculator to help.


To some, “build” may seem like the obvious choice for embedding analytics functionality in their application. However, even if it looks like the less costly option from an investment standpoint, it may not be the most worthwhile option. Let’s look at this graph.

Embedded Analytics - 3 year Build vs Buy

Suppose the desired functionality requires one full-time developer to go to market in 8 months (equivalent to $100,000). And, it takes one-third of their time to support and enhance the capabilities in subsequent years ($40,000 annually). Adding up the technology, UX/UI, platform and management cost. We end up with a total of around 259K for year 3. 

Now with the equivalent “buy” you don't have to any of the above-mentioned costs. It is only technology and licensing costs, which amount to approximately 180K depending on company size and the number of users for year 3. The development time goes down from 8 months to one month. With lower initial cost and quickly go to market, you break-even much sooner than if you decided to build. 

This is probably an oversimplified example, but the point is to assess both the benefits and costs when building a business case based on a comparison of ROI.

Great! You’ve decided to invest in embedded analytics. Now what?



Finding the right solution

Evaluation Criteria

Picking the right solution involves thoroughly evaluating the technology, understanding the expertise offered by the vendor, and implementing a process to ensure success.

Let’s examine the evaluation criteria that is critical to embedded analytics implementations. 

  • Self-Service Capabilities: These are the core capabilities you will make available to your non-technical users. These may include dashboards and reports as well as the interactive and analytical functions they can perform.

  • Scalability: While the solution you choose will connect to your current data environment and meet your data security needs, it should also be flexible enough to meet future demands as your data evolves.

  • Integration: One of the major ways embedded analytics initiatives differ from standalone analytics projects is the need to integrate with the application environment. This means providing a white labeled solution to meet evolving business requirements.

  • Deployment: Since time-to-value is so critical to the success of the project, having a development environment where you can create, style, embed, deploy, and iterate on embedded analytics will enable your team to deliver the functionalities that users want.

  • Customer Support: Choosing the right partner is not simply about the technology; it’s also about finding the level of expertise you require for training, support, and services, as well as aligned business terms that ensure shared success.

These are the core capabilities for all the end-users of your application. During your evaluation, make sure that the ones that are important to your project are demonstrated and understand how you will deliver them in your application. Here is a simple questionnaire to fill out to understand the specifics of your requirements.

Embedded analytics implementations place a greater emphasis on customization and integration capabilities compared to standard BI implementations. Application providers typically want to offer a seamless user experience within the context of their existing application and brand. Focus on enhancing the value of your application while minimizing the cost of development.

Choosing the right partner is not just about the technology; it’s also about finding the right level of expertise and commitment to shared success to get you to the finish line and scale your business further.

Evaluating an Embedded Analytics Solution

Now that we’ve established the criteria for evaluating vendors for embedded analytics, let’s look at the overall process that will help you make the best decision for your business.

1.Determine your Goals
Know exactly what you want the embedded analytics solution for. Who will use it? How will it help you? And why do you need a solution right now? Use a balance of quantifiable metrics, like revenue, adoption rate, customer retention, etc., and soft metrics, like user experience, competitive differentiator, customer satisfaction, etc. 

2. Establish the Timeline
Identify the steps you’ll take to reach your goals. Ask yourself, “When do I want to…”

  • Begin the selection process?
  • Have detailed vendor presentations and demos?
  • Finish a proof of concept?
  • Make my final decision?
  • Start development?
  • Release product?


3. Assemble the Team
Determine the stakeholders who need to be involved. Who is going to care about embedded analytics internally (your IT team, product management, the executive team) and externally (your key customers)? Build the business case collectively to get everyone on board before moving forward.

4. Identify Requirements
Review your technical and non-technical requirements, rank and weigh them. Research your competitors and talk to your customers to develop a firm understanding of the capabilities you want to add to your application. Understand how end users will utilize your products and turn those into requirements.

Consider who will use the third-party products internally. Understand their skill sets and identify any potential resource gaps as you move into the evaluation phase.

5. Research Potential Vendors
Utilize independent industry resources like G2 for Business Intelligence and Analytics Platforms report to find potential vendors, check peer reviews and gain a deeper understanding. Pay special attention to vendors who specialize in the OEM market for software providers.

Attend product demonstrations by each vendor to confirm a basic fit. Discuss your requirements and ask each vendor to demonstrate how they would deliver your specific processes and scenarios. Ask tough questions and make sure the vendors show you the functionalities they promise. 

Don’t forget to enquire about pricing or atleast get a ballpark. Stick to your priorities and do not get dazzled by unnecessary features or shiny embellishments the vendors may distract you with. Evaluate each vendor’s ability to make you successful during the implementation process through access to best practices, community, consulting, support, and training.

6. Refocus on your Goals
Embedded analytics is more than just pretty pictures. During your evaluation process, it will be easy to get lost among a dizzying array of charts and graphs. Don’t forget everything we have discussed in this guide. Ultimately, you want to bring value to your application and your users through

  • Embedability: how tightly you’ll integrate analytics into the overall user experience, the existing application and workflow.

  • Customization:  your ability to white-label and control the look and feel of the application to make it your own, and tailor the functionality so every user has access to the capabilities they need.

  • Scalability:  gives you the ultimate flexibility to create a unique application experience so you will stand out from the crowd, as well as the ability to future-proof your solution so you can tackle any new requirement.

7. Complete Technical Evaluations with a Select Few
After product demos, narrow down your list to the top two or three vendors and begin a structured evaluation process with each one. After understanding and evaluating all the needed features, it becomes about sticking to the timeline that you have in mind.

Dive deeper into the product with an assisted trial, where support is generally available if you run into issues and proceed to a true structured evaluation where you and the vendor are building a proof of concept together. Always implement the proof of concept in a technical environment that is as close to the production environment as possible. 

Connect the embedded analytics solution to your data sources, integrate it with your security, and embed it into your application. If you host a SaaS application in the cloud, do not simply evaluate desktop tools or run analysis off a cleansed spreadsheet – unless that is what you expect your customers to do.

At the end of the evaluation, present the output back to your stakeholders to get feedback and validate your direction.

8. Check Peer Reviews 
Talk to other companies who have used the same vendor to get a better understanding of their experience. 

Ask your vendors for references. Solicit feedback from others in your personal and social networks. Look for references that are similar to your organization in industry, size and use case. 

Don’t just ask whether they’re happy with the vendor. Quiz them about the functionalities the vendor has delivered, the nature of support and training, the duration of deployment, and the roadblocks they have encountered. Understand how the vendor handled problems or issues.

9. Select a Vendor and Get Started
You are finally ready to get the ball rolling! Select the vendor you feel most confident in as a partner to reach your goals. Look beyond the software, for the vendor who gives you the highest chance of success.

Make sure your embedded analytics vendor has the resources to help you, now and in the future as your company grows. Later on, you’ll appreciate being able to test ideas and leverage best practices as your needs evolve.

Get training for those who will be using the platform to create analytics. Create your first set of reports. Work with your vendor’s enablement and consulting teams to understand embedded analytics best practices.

10. Monitor, Adapt and Optimize
There’s a lot that can be said here, given the endless possibilities that come from using embedded analytics. But here are a few general and useful tips:

  • Invest in the training you need to best utilize the embedded analytics solution.
  • After four to five months, touch base with your vendor and let them know how the solution is working for you. 
  • Suggest ideas for new features that you think will be helpful for you and others in your industry.

Using Embedded Analytics for success

You have embedded analytics within your application, where do you go from here?

Here are two ways you can use embedded analytics to improve your product and revenue,

  • Promotion: Generate excitement from your customer and user community about embedded analytics and the value it brings.

  • Pricing and Packaging: For commercial application providers, create a shared value proposition for monetizing your embedded analytics offering.

It is about bringing value to owners of internal IT applications which are looking to promote and build a user base for the embedded analytics rollout.

Embedded analytics is a journey. More often than not, once your customers see data in new and exciting ways, their thirst for more data and insights will only grow.

So be sure to monitor usage, actively acquire feedback from all stakeholders, and adapt your activities to optimize the success of your project over time. Listen to the sales team and prospects, as their feedback will inform future product development plans.


Generate hype around the new features that will help your customers better visualize data and gain actionable insights. 

Show them visuals
Analytics are inherently visual, so the best way to showcase your new capabilities is to show them the visualizations they can expect. Feature screenshots in your promotional materials website and presentations. Employ videos and webinar sessions to guide users through new features and give them an understanding of how they could use them. Make them feel like it is a must-have for every growing brand. 

Leverage customer stories
When communicating the value of embedded analytics in your application, nothing is more convincing than customer testimonials. Reach out to your customers regularly to get feedback, and ask if they would like to do a case study, webinar, or press release. Consider creating a Customer Success Gallery on your website to present all of your success stories in one place. Your customers are often the strongest advocates of your product.

Educate your customers
Today people like being educated, not sold to. Prospects should recognize your company as an industry leader with a compelling product that solves their challenges before they agree to a sales pitch. Educate your potential customers through engaging content aligned with each stage in the buying process, use white papers, solution briefs, and product demonstrations. Create content to generate increased interest in your embedded analytics offering.

Equip internal stakeholders with the tools they need to successfully communicate compelling value to external customers. Think beyond sales enablement. Ensure that teams involved in training, professional services, and marketing understand the embedded analytics value proposition and know how to best utilize the assets available to them.

Pricing Embedded Analytics

Determining the value of embedded analytics to your customers is key to monetizing your offering.

Typically, you want to relate the value that your customer receives to the price you offer for the product. Get down to the numbers. Software providers revealed these key insights when it comes to embedded analytics:

The value of embedded analytics relative to the value of the application has increased by 20% in two years. 93% of commercial SaaS providers say embedded analytics has helped them increase revenue. They charge an additional 25% on top of their core product offering, up from 15% last year.

The value is increasing over time, so the minimum value and functionality customers expect from your application is increasing. For anyone still on the fence about committing to or investing in analytics, there is real danger of being left behind.

With regard to the pricing structure, charging a percentage of the core product is not the only approach. Analytics pricing can also be based on the number of users, overall system usage, or simply a fixed dollar amount. There are multiple factors that go into determining the pricing metrics, including how the pricing fits into your existing pricing structure.

Start your pricing exploration with the question, “what is the value of embedded analytics to your customers relative to the overall value of your application?”

There are three common pricing models for embedded analytics: All-inclusive, module, and tiered. This is how they stack up

All embedded analytics functionalities are a standard part of the product, rather than charged separately.


  • You are clearly communicating the value of analytics in your product.
  • Customers will feel like you are giving them more functionalities than they are paying for, increasing their lifetime value. 



  • Customers who do not use analytics, will not want to “paying” for it and would rather have it be an opt-in service.
  • Analyzing revenue due to embedded analytics becomes harder. .
  • There is no upsell opportunity.
  • It doesn’t take into account the level of usage or number of users.


Analytics capabilities are packaged into an offering that is a separate module. Like an add-on with advanced capability that you charge for versus a basic capability, all customers have access to.


  • Simple packaging model for customers to understand.
  • You have a path to upsell customers 
  • Makes it easy to account for embedded analytics revenue.



  • Customers may feel nickel-and-dimed, especially existing customers. 
  • It becomes harder to manage customer expectations.


Tiered Model
If you already have a tiered offering for your product, you can package analytics functionality into each level. If you don’t have a tiered pricing model, using it just for embedded analytics is a harder sell. 


  • You map capabilities to value and justify the pricing.
  • There is a clear path to upsell.



  • Understanding the revenue impact is harder than module pricing but easier than all-inclusive.
  • If the customer doesn't understand the value, they will feel like they are overpaying. It comes down to communication.


Keep in mind that how you package the offering can be used as a competitive tool, as well. So be sure to take into account your own positioning in the marketplace. For example, if your competitors have some level of embedded analytics, customers will likely expect that you deliver that minimum capability at a similar or lower price. Another scenario could be that everyone in your industry charges for analytics separately, and in order to stand out in the marketplace, you can decide to bundle your capabilities in an all-inclusive offering.

The future of embedded analytics: Data storytelling

Embedded analytics is a dynamic industry, and we’re seeing new capabilities all the time. The key trend that will drive this evolution over the next few years: Data Storytelling.

Data Storytelling is the ability to tell a story with data and to personalize the data seen according to the audience.  

The general way of Data visualization, which is the art of telling numbers in a clear and pedagogical way, is not enough anymore. Though it allows you to communicate complex figures and information by transforming them into visual objects, it is aimed at the Data and Business Intelligence departments of large companies. Today all employees want to know more about the company’s activity, through the data collected every day. This is where data storytelling comes in. 

To illustrate, a Marketing Director doesn’t have the same reporting needs as an operational manager responsible for digital campaigns. With data visualization alone, the Marketing Director will not be able to get a separate view of the data. But with Data storytelling, the Director can customize their data reports depending on the data needs to get actionable insights. 

Data Storytelling has many advantages:

  • Turn your data into action. With clear and usable data at your disposal, you are able to quickly identify trends and possible strategies for your business.
  • Improve the productivity of your teams. Data is automatically presented in a simple and interactive way. Their time is thus concentrated on tasks with high added value.
  • Find more agility in your decision-making. Thanks to a simple and easy-to-use tool in a context where decisions must be made more and more quickly, you reconcile your CIO and the business teams.


More Sophisticated Analytics Capabilities

Over time, we will see the analytic capabilities themselves get more sophisticated. Gartner describes the journey that organizations take in their analytics maturity model.

 Embedded Analytics - Future

  • Descriptive Analytics: Describe what’s happening (e.g., sales are going up, and here’s a chart depicting that trend)

  • Diagnostic Analytics: No longer just describing, now explaining why things happen (e.g., West Coast sales have plummeted because of bad weather)

  • Predictive Analytics: Here’s what the next quarter is going to look like

  • Prescriptive Analytics: Here’s what the future looks like, and here’s what you should do about it

The future for XaaS is embedded analytics. Application providers continue to mature the way they embed analytics into their product as well as the self-service functionality and analytics capabilities they offer. All these innovations will make your application more valuable to your users. And in the end, isn’t embedded analytics a means to make your product the best it can be?


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